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Marginal Likelihood and Bayes Factors for Dirichlet Process Mixture Models
Sanjib Basu and Siddhartha Chib
Journal of the American Statistical Association
Vol. 98, No. 461 (Mar., 2003), pp. 224-235
Stable URL: http://www.jstor.org/stable/30045209
Page Count: 12
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We present a method for comparing semiparametric Bayesian models, constructed under the Dirichlet process mixture (DPM) framework, with alternative semiparameteric or parameteric Bayesian models. A distinctive feature of the method is that it can be applied to semiparametric models containing covariates and hierarchical prior structures, and is apparently the first method of its kind. Formally, the method is based on the marginal likelihood estimation approach of Chib (1995) and requires estimation of the likelihood and posterior ordinates of the DPM model at a single high-density point. An interesting computation is involved in the estimation of the likelihood ordinate, which is devised via collapsed sequential importance sampling. Extensive experiments with synthetic and real data involving semiparametric binary data regression models and hierarchical longitudinal mixed-effects models are used to illustrate the implementation, performance, and applicability of the method.
Journal of the American Statistical Association © 2003 American Statistical Association